The present disclosure relates, generally, to systems and method for processing optical images. More particularly, the disclosure relates to automatic detection of polyps in optical images or video.
The colonoscopy is a preferred procedure for cancer screening and prevention, being widely accepted as effective for detecting colonic polyps, which are precursors to colon cancer. During the procedure, an endoscope fitted with a small camera is guided through a patient's colon to provide a real-time optical feed for diagnosis, biopsy and removal of polyps. As a result of colonoscopy use, the rate of incidence and mortality due to colon cancer has seen a significant decline.
However, the colonoscopy is an operator-dependent procedure, and as such relies on the effectiveness of the clinician to identify polyps. Hence, fatigue and insufficient attentiveness during examination, particularly during back-to-back procedures, often play a role in missing polyps. By some estimates the average polyp miss-rate is between 4 and 12%. Patients with missed polyps may be diagnosed with a late stage cancer with the survival rate of less than 10%. As such, the importance of reducing miss-rates cannot be overemphasized.
Computer-aided polyp detection can provide a powerful tool to help reduce polyp miss-rate and encourage attentiveness during these procedures. To this end, algorithms have been developed to analyze in real-time the video feed acquired during the colonoscopy. Specifically, early attempts have focused on color and texture features to identify polyps in colonoscopy images. However, the effectiveness of such methods is limited due and color variations and texture visibility of polyps. More recent approaches have considered shape, or geometrical appearance features, such as elliptical-shaped features, or valley information to localize polyps. Yet other approaches have considered, spatio-temporal features.
As well known to those skilled in the art, computer-aided polyp detection based on the above approaches remains a challenging task. This is because various features appearing on a video feed can vary considerably, and depend on the camera viewing angle, depth of field, and illumination. For instance, polyp color can may have different colors, ranging from dark to saturated, depending on illumination. On the other hand, polyp texture becomes fully visible only if a given polyp appears within the depth of field of the camera. In addition, geometric features in the absence of contextual clues can be misleading. For example, valley information may result in false detections particularly around wrinkles and vascular structures. Furthermore, spatio-temporal features are only suitable for use in off-line processing, since information from the past and future frames are typically utilized.
In light of the above, there remains a need for systems and methods capable of accurately detecting polyps during a colonoscopy procedure.